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SAP Data Warehouse Cloud – Experiences in building a data model

You use purchasing data in your reporting? Benefit from our free template! You need more data? Benefit from our experience! SAP Data Warehouse Cloud allows you to expand central data stores easily and intuitively.

Introduction to the SAP Data Warehouse Cloud (DWC)

The SAP Datawarehouse Cloud solution has been on the market since the end of 2019. Currently, and especially in the near future, the focus of SAP products is on cloud-based solutions. Specifically, this means that such solutions are being actively developed further by SAP. We at CubeServ have already written various articles on this topic, gain a deep insight into the SAP Data Warehouse Cloud already today with the following links.
The SAP Data Warehouse Cloud solution is ideal for agile applications because you can add data from various sources quite quickly and simple tools are available for modeling. Until now, complex steps were necessary to map a simple data model in a system like BW. In addition to the development effort, the required competencies as well as the dependency on central IT were a major hurdle.

Use of prefabricated data models – Partner Contents for SAP Data Warehouse Cloud (DWC)

You do not have to perform the necessary data modeling yourself in all cases. Contents are available in the SAP App Center that you can purchase. Some of the contents are chargeable and you can choose between different pricing models. For example, with one model you pay a one-time price, and with the other you can expect recurring fees if you want to continue using the content. Based on best practice experience, CubeServ has developed a data model and the necessary dashboards with associated KPI’s for purchasing to save you time in modeling. The product description can be found at the following link:
This blog is about openly sharing the experience we gained from modeling.

How does the business unit obtain the source data (ERP/BW data) and how can it be enriched?

The connection can be set up yourself in a few steps (as long as the connection data is available) or you can get one-time help from the system administration of the source system (usually IT department). You can persist data or make the data available in the DWC via live connection. You have the option to extend your model with your own Excel files at any time. If customizing tables are imported into the cloud, these tables are referred to as “local tables”. The integration of further data can also be done via the DWC (storage in the cloud) or via storage in the source system (e.g. an SAP HANA). The DWC allows it with a few steps, when storing or connecting via SAP HANA, the storage in the cloud can be prevented. I could do the source system connection myself, without basic support and without knowing the DWC for a long time.

SAP HANA Smart Data Integration (SDI):

For connections to local source systems, SAP Data Warehouse Cloud uses SAP HANA Smart Data Integration (SDI) and its Data Provisioning Agent (DP Agent), which acts as a gateway to SAP Data Warehouse Cloud. The DP Agent hosts all SDI adapters and works as a communication interface between the SAP Data Warehouse Cloud, and the adapter. For the Data Warehouse Cloud, the HANA, ABAP and OData adapters are available. The connection is specific to a Space. The user creates a connection from a Space. All users assigned to the Space can use the connection. Our content is based on ERP tables. In our case, an ERP on HANA system was used. The data was not replicated to DWC, but made available via remote tables in DWC.
Customizing: The content includes a customer customizing table (rating scale) created in Excel so that you can individually rate the delivery reliability. With the customizing table it is possible to define the value limits for delivery reliability classes. The Excel file can be exported from DWC, customized locally and imported back into DWC.
Entity-Relationship Modeling: Data integration is easily implemented in DWC by dragging and dropping the necessary elements. When two tables are linked, DWC usually recognizes the relationships if the labels are similar. So if you need additional attributes for purchasing documents, like vendor information, you can add them quickly and easily. In most cases DWC already suggests the links. Besides that you have the possibility to start from scratch. This means that first a logical data model is defined with the necessary dimensions. In practice, for example, the marketing department together with the controlling department could create the logical data model. Only in the next step the necessary tables for the model are defined and created by the controlling department. In this phase, the logical data model becomes a physical data model.

Functions

What functions are available to the user for modeling?
  • Graphical modeling (intuitive and without much previous knowledge)
  • SQL modeling (SQL know-how necessary)
  • Joins
  • Calculated columns
  • Hide or rename columns
  • Filter (using simple SQL expressions)
  • Data preview (limited to 1000 records)

How is data visualization implemented in the SAP Data Warehouse Cloud?

For the visualization you create stories. The stories can contain multiple sheets and the sheets can contain multiple tables or graphs. Most conventional functions are available to you. The SAP Data Warehouse comes with five free licenses for SAP Analytics Cloud as a front end in the paid version.

How do you keep track of your modeling?

Over time, many models, tables, views and stories exist. In order to find your objects again, it is recommended to define a strict naming convention for yourself or in the department.
Business Catalog: The Business Catalog lists all the objects that are accessible to you. You can add a suitable description for each object. We recommend to use this function. The following is an excerpt from the content of our Procurement Cockpit.

How do you create a common understanding about your data across the organization?

With the Space concept of SAP Data Warehouse Cloud, the required data is unlocked in the respective spaces and made available to the end user. The administration (costs and access control) of the Spaces can be done by the department. We suggest that each department has its own Space available. In addition, it would be useful to provide a global Space for rolling out company-wide reports. A Space can be connected to one or more other Spaces in order to be able to use data and models across Spaces. This provides transparency by allowing everyone involved to view the data flow and to track enhancements or calculations. In addition, the Entity Relationship Model (ERM) gives you another tool for collaborative work. ERM offers two advantages at once. On the one hand, a logical data model as well as an existing physical data model can be mapped graphically. Furthermore, you can quickly and easily give an introduction to the existing data model or conveniently create a new logical data model yourself or in a team.

Conclusion

With the SAP Date Warehouse Cloud, new approaches are available which enable you (the business department) to create and publish your own data models quickly and independently of IT. Even ad-hoc analyses, which until now were mostly performed in Excel, could become superfluous with the use of DWC, since these analyses are possible directly in DWC. I was surprised how easy and fast it was to create models with the help of graphical modeling. The response times of the system in this development phase were very good. Even with large data models, the user interface is still easy to use and provides a very good overview. At the moment, complex calculations in the aggregation are still written in SQL. For the end user it would make sense if these functions were available in the graphical modeling tool. Intermediate results cannot be persisted in the current release. This could still be supported to simplify modeling and increase performance. In return, however, live models can be created and thus provide access to real-time data. The performance when drilling down hierarchies in the dashboard could still be improved, but currently the response times here are perfectly acceptable. DWC is an extremely interesting alternative to PowerBI and Tableau. I am curious about the further developments of this solution.

Arrange now your Expert Call. We look forward to hearing from you.

Aginthan Thavarajasingam

My passion is to provide strong and sustainable Solutions for Analytics department to turn Data into Information - more efficient and more pleasant. # Self-Service-BI
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Expert Team
Benedikt Bleyer
Adrian Bourcevet
Roland J. Merz
Drazen Pavlovic
Julian Rudack
Jan Frederick Sabjetzki
Thorsten Stossmeister
Aginthan Thavarajasingam
Jan Wiesemann